1 About

This data notebook is based on a model presented at the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.

If you want to cite the method/model please use:

Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at the International Conference on Evolving Cities, MAST Mayflower Studios, Southampton, United Kingdom. 22 - 24 Sep 2021.

If you are interested in how the model works start from https://dataknut.github.io/localCarbonTaxModels/

2 Citing this data notebook

If you wish to re-use material from this data notebook please cite it as:

Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale: Wider Solent/‘Pan-Hampshire’ as a case study, University of Southampton, United Kingdom

License: CC-BY

Share, adapt, give attribution.

3 Highlights

This data notebook estimates the value of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. These emissions are all consumption, gas and electricity. It does this under two scenarios - a simple carbon value multiplier and a rising block tariff.

It then compares these with estimates of the cost of retrofitting EPC band dwellings D-E and F-G in each LSOA and for the whole area under study.

Key results:

Figure 5.3 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e - which areas emit the most?
Figure 5.20 maps the total retrofit costs
Table 5.13 compares the total retrofit costs with the year 1 levy under Scenario 1 while Table 5.14 does the same under Scenario 2.
Figure 5.21 shows the years to pay back under Scenario 1 for an all emissions levy while Figure 5.24 does the same for Scenario 2
Figure 5.22 shows what would happen after year 1 if the levy were shared equally across LSOAs (all emissions, Scenario 1) and Figure 5.25 shows the same for Scenario 2.
Table 5.16 shows the final results for Scenario 1
Figure 5.27 compares the levy scenarios at local authority level
Figure 5.28 shows payback years under each Scenario assuming a constant all emissions levy

4 Introduction

Background blurb about emissions, retofit, carbon tax/levy etc

5 Emissions Levy Case Study - Wider Solent/‘Pan-Hampshire’

In the reminder of this paper we develop a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. We apply carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. We then sum these values to given an overall levy revenue estimate for the area in the case study.

We then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.

We then compare the distributions of the two to understand whether sufficient revenue would be generated within each LSOA or in the case styudy area to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so we also analyse the extent to which redistribution of revenue from high emissions areas (households) would be required. It should be noted that this is area level analysis using mean emissions per household. It will not capture within-LSOA hetergeneity in emissions and so will almost certaonly underestimate the range of the household level emissions levy value.

5.1 Data and boundary files

We will use a number of datasets to analyse the patterns of emissions. Some of these are in the repo as they are public access, others are not (or too large).

This analysis is at LSOA level.

5.1.1 Useful LSOA labels and codes

Load LSOA look-up table and list the local authoroties we are covering.

## Loading LSOA look-up table with useful labels

## [1] "LSOA11CD"        "LSOA11NM"        "MSOA11NM"        "LA11NM"         
## [5] "WD20CD"          "WD20NM"          "RUC11"           "Supergroup Name"
## [9] "LAD11NM"

## Number of LSOAs covered

## [1] 1194

## Number of local authorities covered

## [1] 14

## Using 'n_LSOAs' as value column. Use 'value.var' to override

Table 5.1: Number of LSOAs by local authority and urban/rural classification
LA11NM	Rural town and fringe	Rural village and dispersed	Urban city and town
Basingstoke and Deane	15	14	80
East Hampshire	13	11	48
Eastleigh	6		71
Fareham			73
Gosport			53
Hart	10	8	39
Havant		2	76
Isle of Wight	19	9	61
New Forest	18	13	83
Portsmouth			125
Rushmoor			58
Southampton			148
Test Valley	5	18	48
Winchester	21	18	31

5.1.2 Boundaries

LSOA - this is all going to be LSOA analysis

## Loading Solent LSOA boundaries from file

## Rows of data: 1136

Check with a map…

## Boundary data co-ord system: 27700

Figure 5.1: LSOA check map (shows LSOA, MSOA and ward names when clicked

5.1.3 IMD 2019

Labeled as 2019 but actually 2018 data. Source: https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019

## Overall IMD decile counts

## [1] 32844

## 
##   1 (10% most deprived)                       2                       3 
##                    3284                    3284                    3285 
##                       4                       5                       6 
##                    3284                    3285                    3284 
##                       7                       8                       9 
##                    3284                    3285                    3284 
## 10 (10% least deprived) 
##                    3285

## # Solent IMD decile counts

## [1] 1194

## 
##   1 (10% most deprived)                       2                       3 
##                      44                      81                      94 
##                       4                       5                       6 
##                     122                     115                     103 
##                       7                       8                       9 
##                     122                     134                     159 
## 10 (10% least deprived) 
##                     220

## 
##   1 (10% most deprived)                       2                       3 
##              0.03685092              0.06783920              0.07872697 
##                       4                       5                       6 
##              0.10217755              0.09631491              0.08626466 
##                       7                       8                       9 
##              0.10217755              0.11222781              0.13316583 
## 10 (10% least deprived) 
##              0.18425461

## 
## 50% least deprived  50% most deprived 
##                738                456

## 
## 50% least deprived  50% most deprived 
##          0.6180905          0.3819095

These are LSOA level deprivation indices. Decile is the English & Welsh decile:

1 = 10% most deprived LSOAs in England & Wales;
10 = 10% least deprived LSOA in England & Wales.

Figure 5.2: LSOA IMD map (shows LSOA, MSOA, ward names and IMD decile when clicked

5.1.4 Fuel poverty

2019 estimates - do we actually use this data?

Source: https://www.gov.uk/government/statistics/sub-regional-fuel-poverty-data-2021

5.2 CREDS place-based emmissions estimates

See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/

“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”

“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."

Source: https://www.carbon.place/

Notes:

Emissions are presented as per capita…
Appears to be based on residential/citizen emissions only - does not appear to include commercial/manufacturing/land use etc

## [1] 32844

Table 5.2: Data summary
Name	credsLsoaDT
Number of rows	1194
Number of columns	30
Key	LSOA11CD
_______________________
Column type frequency:
character	7
factor	1
numeric	22
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	n_unique
LAD11NM	1	4	21	14
WD18NM	1	4	42	292
LSOA11CD	1	9	9	1194
LSOA11NM	1	9	26	1194
WD20CD	1	9	9	282
RUC11	1	19	27	3
oacSuperGroupName	1	15	35	8

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
IMD_Decile_label	0	1	FALSE	10	10 : 220, 9: 159, 8: 134, 4: 122

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
CREDStotal_kgco2e	0	1.00	15183049.41	5154042.47	4818400.00	11606400.00	14731500.00	18590000.00	37236000.00	▅▇▅▁▁
CREDSgas_kgco2e2018	0	1.00	1543093.13	601806.98	0.00	1199077.50	1495220.00	1849125.00	5108580.00	▂▇▂▁▁
CREDSelec_kgco2e2018	0	1.00	782210.06	266915.81	350200.00	613800.00	714560.00	872990.00	2478420.00	▇▃▁▁▁
CREDSotherEnergy_kgco2e2011	0	1.00	136491.84	318307.76	0.00	22221.50	40244.50	76161.25	2315490.00	▇▁▁▁▁
CREDSmeteredHomeEnergy_kgco2e2018	0	1.00	2325303.19	701431.75	622710.00	1865605.00	2212690.00	2657880.00	7587000.00	▅▇▁▁▁
CREDSallHomeEnergy_kgco2e2018	0	1.00	2461795.03	754979.54	1048950.00	1963222.50	2321827.00	2811340.00	8553640.00	▇▅▁▁▁
CREDScar_kgco2e2018	0	1.00	1861521.96	714842.21	333540.00	1357290.00	1768650.00	2237750.00	5014000.00	▃▇▃▁▁
CREDSvan_kgco2e2018	0	1.00	305585.01	898342.37	11328.00	126901.25	183270.00	278405.00	18232800.00	▇▁▁▁▁
pop_2018	0	1.00	1663.18	391.28	947.00	1430.00	1590.00	1780.00	5620.00	▇▂▁▁▁
energy_pc	28	0.98	17.39	5.40	6.67	13.52	16.40	20.47	44.48	▅▇▂▁▁
pc_Heating_Electric	0	1.00	9.53	9.42	0.13	3.48	6.37	12.52	85.27	▇▁▁▁▁
epc_total	0	1.00	422.65	180.14	153.00	309.00	376.00	492.00	2350.00	▇▁▁▁▁
epc_newbuild	0	1.00	80.15	127.29	1.00	27.00	46.00	80.00	1960.00	▇▁▁▁▁
epc_A	0	1.00	0.78	3.15	0.00	0.00	0.00	0.00	48.00	▇▁▁▁▁
epc_B	0	1.00	56.91	114.81	0.00	7.00	21.00	60.00	1800.00	▇▁▁▁▁
epc_C	0	1.00	128.94	78.50	18.00	74.00	108.00	161.00	688.00	▇▃▁▁▁
epc_D	0	1.00	163.42	47.81	4.00	134.00	160.00	185.00	413.00	▁▇▅▁▁
epc_E	0	1.00	55.72	32.21	0.00	34.00	50.00	72.00	243.00	▇▇▁▁▁
epc_F	0	1.00	13.26	16.59	0.00	4.00	8.00	16.00	196.00	▇▁▁▁▁
epc_G	0	1.00	3.63	6.04	0.00	0.00	2.00	4.00	96.00	▇▁▁▁▁
IMD_Decile	0	1.00	6.47	2.78	1.00	4.00	7.00	9.00	10.00	▂▅▅▆▇
IMDScore	0	1.00	16.77	12.38	0.95	7.14	13.39	22.90	72.08	▇▅▂▁▁

## 
## Basingstoke and Deane        East Hampshire             Eastleigh 
##                   109                    72                    77 
##               Fareham               Gosport                  Hart 
##                    73                    53                    57 
##                Havant         Isle of Wight            New Forest 
##                    78                    89                   114 
##            Portsmouth              Rushmoor           Southampton 
##                   125                    58                   148 
##           Test Valley            Winchester 
##                    71                    70

## [1] 1194

Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings

Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)

First check the n electricity meters logic…

## LSOAs (check):

## [1] 1194

## [1] 1194

Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.

That assumption seems sensible…

5.2.1 Estimate per dwelling emissions

We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.

## # Summary of per dwelling values

Table 5.3: Data summary
Name	…[]
Number of rows	1194
Number of columns	9
Key	NULL
_______________________
Column type frequency:
numeric	9
________________________
Group variables	None

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
CREDStotal_kgco2e_pdw	1	21834.16	8865.44	5802.13	15089.90	20566.08	27272.40	52773.03	▅▇▅▂▁
CREDSgas_kgco2e2018_pdw	1	2150.21	726.47	0.00	1784.09	2175.64	2624.55	4333.54	▁▂▇▃▁
CREDSelec_kgco2e2018_pdw	1	1072.63	243.32	582.86	933.02	1012.61	1115.83	2272.31	▂▇▁▁▁
CREDSmeteredHomeEnergy_kgco2e2018_pdw	1	3222.84	728.77	1123.58	2738.86	3166.69	3689.29	6404.30	▁▇▆▁▁
CREDSotherEnergy_kgco2e2011_pdw	1	180.66	404.84	0.00	32.77	54.67	103.88	2984.01	▇▁▁▁▁
CREDSallHomeEnergy_kgco2e2018_pdw	1	3403.50	737.23	1506.11	2873.42	3305.60	3858.38	6790.95	▂▇▅▁▁
CREDScar_kgco2e2018_pdw	1	2621.81	959.27	457.84	1909.43	2523.53	3295.55	5174.83	▂▇▇▅▂
CREDSvan_kgco2e2018_pdw	1	409.99	1109.82	22.61	180.18	255.48	394.30	32186.23	▇▁▁▁▁
CREDSpersonalTransport_kgco2e2018_pdw	1	3031.80	1488.24	528.81	2144.14	2885.36	3714.77	33215.59	▇▁▁▁▁

Examine patterns of per dwelling emissions for sense.

5.2.1.1 All emissions

Figure 5.3 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.

## Per dwelling T CO2e - all emissions

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 5.3: Scatter of LSOA level all per dwelling emissions against IMD score

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDStotal_kgco2e_pdw
## t = -28.11, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6643148 -0.5959946
## sample estimates:
##        cor 
## -0.6313783

##    LAD11NM             WD18NM            LSOA11CD         All_Tco2e_per_dw
##  Length:1194        Length:1194        Length:1194        Min.   : 5.802  
##  Class :character   Class :character   Class :character   1st Qu.:15.090  
##  Mode  :character   Mode  :character   Mode  :character   Median :20.566  
##                                                           Mean   :21.834  
##                                                           3rd Qu.:27.272  
##                                                           Max.   :52.773

Table 5.4: Lowest all emissions
LAD11NM	WD18NM	LSOA11CD	All_Tco2e_per_dw
Isle of Wight	Newport East	E01017332	5.8
Southampton	Bargate	E01017140	5.8
Portsmouth	Charles Dickens	E01033381	6.1
Portsmouth	St Thomas	E01017133	6.4
Southampton	Bargate	E01017139	7.0
Isle of Wight	Newport Central	E01017326	7.0

Table 5.4: Highest all emissions
LAD11NM	WD18NM	LSOA11CD	All_Tco2e_per_dw
Hart	Hartley Wintney	E01022858	52.8
Hart	Odiham	E01032855	51.6
Basingstoke and Deane	Hatch Warren and Beggarwood	E01022511	51.1
Hart	Fleet East	E01032857	50.7
Basingstoke and Deane	Hatch Warren and Beggarwood	E01022512	48.9
Fareham	Fareham North	E01022729	48.5

Are there any strange outliers?

Figure 5.4 maps retrofit costs

Figure 5.4: Total emissios

5.2.1.2 Home energy use

Figure 5.5 uses the same plotting method to show emissions per dwelling due to gas use. This preserves the negative correlation shown in the previous plot for ‘all emissions’ but with some variation, notably for LSOAs which have a higher % ofelectric heating.

## Per dwelling T CO2e - gas emissions

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       0    1784    2176    2150    2625    4334

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 5.5: Scatter of LSOA level gas per dwelling emissions against IMD score

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDSgas_kgco2e2018_pdw
## t = -21.557, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5692532 -0.4875429
## sample estimates:
##        cor 
## -0.5296256

Are there any strange outliers? Could be an issue with the gas meter data to LSOA allocation…

Figure 5.6 uses the same plotting method to show emissions per dwelling due to electricity use. This is usuallu much more random… although note the LSOAs with higher % electric heating.

## Per dwelling T CO2e - elec emissions

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 5.6: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDSelec_kgco2e2018_pdw
## t = -9.5232, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3178384 -0.2123733
## sample estimates:
##        cor 
## -0.2659013

Figure 5.7 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - elec emissions

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 5.7: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDSelec_kgco2e2018_pdw
## t = -9.5232, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3178384 -0.2123733
## sample estimates:
##        cor 
## -0.2659013

Table 5.5: Mean per dwelling emissions by energy source for urban vs rural areas
RUC11	mean_gas_kgco2e	mean_elec_kgco2e	mean_other_energy_kgco2e
Rural town and fringe	2,414.5	1,120.4	229.9
Rural village and dispersed	1,415.9	1,694.1	1,249.0
Urban city and town	2,190.5	1,009.3	75.4

Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$CREDStotal_kgco2e_pdw and credsLsoaDT$CREDSmeteredHomeEnergy_kgco2e2018_pdw
## t = 26.567, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5729367 0.6442871
## sample estimates:
##       cor 
## 0.6098462

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Strong correlation. So in theory we could (currently) use measured energy emissions as a proxy for total emissions.

Repeat for all home energy - includes estimates of emissions from oil etc

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$CREDStotal_kgco2e_pdw and credsLsoaDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 34.154, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6734136 0.7308495
## sample estimates:
##       cor 
## 0.7032774

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Slightly weaker correlation…

5.2.1.3 Transport

We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)

Figure 5.8 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - car emissions

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 5.8: Scatter of LSOA level car use per dwelling emissions against IMD score

## Correlation with IMD score (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDScar_kgco2e2018_pdw
## t = -30.024, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6873457 -0.6226502
## sample estimates:
##        cor 
## -0.6562022

##                          RUC11 mean_car_kgco2e mean_van_kgco2e
## 1:       Rural town and fringe        3085.434        370.1960
## 2: Rural village and dispersed        3977.615        745.3948
## 3:         Urban city and town        2445.047        382.8929

Figure 5.9 uses the same plotting method to show emissions per dwelling due to van use.

## Per dwelling T CO2e - van emissions

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 5.9: Scatter of LSOA level van use per dwelling emissions against IMD score

## Correlation with IMD score (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDSvan_kgco2e2018_pdw
## t = 0.99035, df = 1192, p-value = 0.3222
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.02810456  0.08526582
## sample estimates:
##        cor 
## 0.02867284

Table 5.6: Lowest all emissions
LAD11NM	WD18NM	LSOA11CD	CREDSvan_kgco2e2018_pdw
Southampton	Bargate	E01017137	22.6
Southampton	Bargate	E01017138	34.1
Portsmouth	Charles Dickens	E01033381	43.3
Southampton	Bargate	E01032755	43.9
Portsmouth	St Thomas	E01017130	45.5
Basingstoke and Deane	Eastrop	E01032842	50.5

Table 5.6: Highest all emissions
LAD11NM	WD18NM	LSOA11CD	CREDSvan_kgco2e2018_pdw
Portsmouth	Cosham	E01017053	32,186.2
Rushmoor	Wellington	E01023142	13,358.5
Test Valley	Romsey Extra	E01023199	11,077.0
Winchester	Wonston and Micheldever	E01023288	4,671.2
Eastleigh	Chandler's Ford	E01022665	3,603.1
Test Valley	Harroway	E01023180	3,516.3

Are there any strange outliers? Could be an issue with the CREDS model…

5.2.2 Impute EPC counts

In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…

Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.

## N EPCs

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   153.0   309.0   376.0   422.6   492.0  2350.0

## N elec meters

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   325.0   619.2   691.0   731.8   802.0  2638.0

Correlation between high % EPC F/G or A/B and deprivation?

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Now we need to convert the % to dwellings using the number of electricity meters (see above).

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

5.2.3 Estimating the annual emissions levy

Case studies:

Annual carbon tax
Half-hourly (real time) carbon tax (not implemented) - this would only affect electricity

BEIS/ETC Carbon ‘price’

2020 - £21.93 https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/849276/Carbon__Penalty_Price_Determination_for_2020.pdf

EU carbon ‘price’

September 2021: €60 (£51) https://tradingeconomics.com/eecxm:ind

BEIS Carbon ‘Value’ https://www.gov.uk/government/publications/valuing-greenhouse-gas-emissions-in-policy-appraisal/valuation-of-greenhouse-gas-emissions-for-policy-appraisal-and-evaluation#annex-1-carbon-values-in-2020-prices-per-tonne-of-co2

based on a Marginal Abatement Cost (MAC)
2021:
- Low: £122/T
- Central: £245/T <- use the central value for now
- High: £367/T

Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)

5.2.3.1 Scenario 1: central cost

The table below shows the overall £ GBP total for the case study area in £M.

##    beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1:          4441.5            451.4               228.82

The table below shows the mean per dwelling value rounded to the nearest £10.

##    beis_GBPtotal_c_perdw beis_GBPtotal_c_gas_perdw beis_GBPtotal_c_elec_perdw
## 1:                  5350                       530                        260
##    beis_GBPtotal_c_energy_perdw
## 1:                          790

Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 5.10: £k per LSOA revenue using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 5.11: £k per LSOA revenue using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1422    3697    5039    5349    6682   12929

Table 5.7: Highest emissions
LSOA11CD	WD18NM	CREDStotal_kgco2e_pdw	beis_GBPtotal_c_perdw
E01022858	Hartley Wintney	52,773.0	12,929.4
E01032855	Odiham	51,606.1	12,643.5
E01022511	Hatch Warren and Beggarwood	51,113.3	12,522.8
E01032857	Fleet East	50,693.6	12,419.9
E01022512	Hatch Warren and Beggarwood	48,911.4	11,983.3
E01022729	Fareham North	48,537.6	11,891.7

Table 5.7: Lowest emissions
LSOA11CD	WD18NM	CREDStotal_kgco2e_pdw	beis_GBPtotal_c_perdw
E01017332	Newport East	5,802.1	1,421.5
E01017140	Bargate	5,845.6	1,432.2
E01033381	Charles Dickens	6,065.4	1,486.0
E01017133	St Thomas	6,367.7	1,560.1
E01017139	Bargate	7,015.4	1,718.8
E01017326	Newport Central	7,017.0	1,719.2

Figure ?? repeats the analysis but just for gas.

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 5.12: £k per LSOA incurred via gas using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 5.13: £k per LSOA incurred via gas using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0   437.1   533.0   526.8   643.0  1061.7

Table 5.8: Highest gas emissions
LSOA11CD	WD18NM	gasTCO2e_pdw	beis_GBPtotal_c_gas_perdw
E01023171	Chilworth, Nursling and Rownhams	4.3	1,061.7
E01022706	Hiltingbury	4.3	1,055.7
E01022602	Headley	4.2	1,031.8
E01022998	Brockenhurst and Forest South East	4.0	979.8
E01022629	Rowlands Castle	3.9	962.8
E01022855	Odiham	3.9	960.1

Table 5.8: Lowest gas emissions
LSOA11CD	WD18NM	gasTCO2e_pdw	beis_GBPtotal_c_gas_perdw
E01017290	West Wight	0.0	0.0
E01017291	Central Wight	0.0	0.0
E01017297	Central Wight	0.0	0.0
E01017298	Chale, Niton and Whitwell	0.0	0.0
E01017299	Chale, Niton and Whitwell	0.0	0.0
E01022489	Burghclere, Highclere and St Mary Bourne	0.0	0.0

Figure ?? repeats the analysis for electricity.

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 5.14: £k per LSOA incurred via electricity using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 5.15: £k per LSOA incurred via electricity using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   142.8   228.6   248.1   262.8   273.4   556.7

Table 5.9: Highest electricity emissions
LAD11NM	WD18NM	LSOA11CD	elecTCO2e_pdw	beis_GBPtotal_c_elec_perdw
Hart	Hartley Wintney	E01022858	2.3	556.7
Basingstoke and Deane	Burghclere, Highclere and St Mary Bourne	E01022489	2.2	531.1
New Forest	Forest North West	E01023026	2.1	523.8
Basingstoke and Deane	Burghclere, Highclere and St Mary Bourne	E01022514	2.1	519.9
Basingstoke and Deane	East Woodhay	E01022500	2.1	517.8
New Forest	Bramshaw, Copythorne North and Minstead	E01022993	2.1	512.3

Table 5.9: Lowest electricity emissions
LAD11NM	WD18NM	LSOA11CD	elecTCO2e_pdw	beis_GBPtotal_c_elec_perdw
East Hampshire	Whitehill Pinewood	E01022639	0.6	142.8
Southampton	Woolston	E01017278	0.7	160.6
Portsmouth	Charles Dickens	E01017031	0.7	172.8
Portsmouth	St Thomas	E01017132	0.7	174.0
Portsmouth	Hilsea	E01017091	0.7	177.2
Portsmouth	Charles Dickens	E01017035	0.7	179.1

Figure ?? shows the same analysis for metered energy (elec + gas)

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 5.16: £k per LSOA incurred via electricity and gas using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 5.17: £k per LSOA incurred via electricity and gas using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   275.3   671.0   775.8   789.6   903.9  1569.1

Figure ?? shows the same analysis for all home energy (elec + gas + oil etc)

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 5.18: £k per LSOA incurred via all home heat energy using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 5.19: £k per LSOA incurred via all home heat energy using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   275.3   671.0   775.8   789.6   903.9  1569.1

5.2.3.2 Scenario 2: Rising block tariff

Applied at to per dwelling values (not LSOA total)

Cut at 25%, 50% - so any emissions over 50% get high carbon cost

## Cuts for total per dw

##        0%       25%       50%       75%      100% 
##  5802.128 15089.898 20566.083 27272.399 52773.034

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

##           V1 beis_GBPtotal_sc2_l_perdw beis_GBPtotal_sc2_c_perdw
##  1: 19.50899                  1840.968                 1082.6772
##  2: 19.82781                  1840.968                 1160.7888
##  3: 13.99627                  1707.545                    0.0000
##  4: 20.65559                  1840.968                 1341.6653
##  5: 24.81776                  1840.968                 1341.6653
##  6: 23.21860                  1840.968                 1341.6653
##  7: 16.50536                  1840.968                  346.7874
##  8: 12.62994                  1540.852                    0.0000
##  9: 18.11356                  1840.968                  740.7964
## 10: 17.63948                  1840.968                  624.6469
##     beis_GBPtotal_sc2_h_perdw beis_GBPtotal_sc2_perdw
##  1:                   0.00000                2923.645
##  2:                   0.00000                3001.756
##  3:                   0.00000                1707.545
##  4:                  32.84875                3215.482
##  5:                1560.36436                4742.997
##  6:                 973.47545                4156.108
##  7:                   0.00000                2187.755
##  8:                   0.00000                1540.852
##  9:                   0.00000                2581.764
## 10:                   0.00000                2465.615

Table 5.10: Data summary
Name	…[]
Number of rows	1194
Number of columns	3
Key	NULL
_______________________
Column type frequency:
numeric	3
________________________
Group variables	None

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
V1	1	21.83	8.87	5.80	15.09	20.57	27.27	52.77	▅▇▅▂▁
beis_GBPtotal_sc2_perdw	1	4107.16	2776.24	707.86	1842.36	3183.22	5643.85	15002.58	▇▃▂▁▁
beis_GBPtotal_sc2	1	2764975.74	1564529.45	587844.80	1514526.30	2355174.80	3775189.52	7035326.91	▇▆▅▂▂

##    nLSOAs sum_total_sc1 sum_total_sc2
## 1:   1194      4441.497      3301.381

##    CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw
## 1:                2001.180                217.6592
## 2:                2192.751                217.6592
## 3:                2034.557                217.6592
## 4:                2154.426                217.6592
## 5:                2240.187                217.6592
## 6:                2316.899                217.6592

##    CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw beis_GBPgas_sc2_c_perdw
## 1:                2001.180                217.6592                53.18664
## 2:                2192.751                217.6592                95.92818
## 3:                2034.557                217.6592                61.36401
## 4:                2154.426                217.6592                90.73196
## 5:                2240.187                217.6592                95.92818
## 6:                2316.899                217.6592                95.92818
##    beis_GBPgas_sc2_h_perdw beis_GBPgas_sc2_perdw
## 1:                0.000000              270.8458
## 2:                6.281471              319.8688
## 3:                0.000000              279.0232
## 4:                0.000000              308.3911
## 5:               23.690540              337.2779
## 6:               51.843957              365.4313

## [1] 301.6514

## [1] 153.2484

##    nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP
## 1:   1194                3301.381            301.6514             153.2484

5.2.4 Estimate retofit costs

from A-E <- 13300
from F-G <- 26800

Excludes EPC A, B & C (assumes no need to upgrade)

## To retrofit D-E (£m)

## [1] 6259.381

## Number of dwellings: 470630

## To retrofit F-G (£m)

## [1] 961.2963

## Number of dwellings: 35869

## To retrofit D-G (£m)

## [1] 7220.677

## To retrofit D-G (mean per dwelling £k)

## [1] 14.14921

Table 5.11: Summary of retrofit costs (£m)
meanRetrofitPerLSOA_m	totalRetrofit_m
6.0	7,220.7

Plot retrofit costs

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 5.20 maps retrofit costs

Figure 5.20: LSOA retrofit costs (upgrade EPC D to G)

Table 5.12: Lowest retofit cost
LAD11NM	WD18NM	LSOA11CD	epc_pc_A_C	retrofitSum
Basingstoke and Deane	Rooksdown	E01032840	99.5	56,964.2
Hart	Fleet West	E01032854	97.0	250,281.8
Basingstoke and Deane	Popley West	E01032843	97.5	269,283.0
Portsmouth	Charles Dickens	E01033381	93.4	825,292.6
Basingstoke and Deane	Hatch Warren and Beggarwood	E01032849	88.7	854,505.8
Basingstoke and Deane	Brookvale and Kings Furlong	E01032846	92.3	901,769.1

Table 5.12: Highest retofit cost
LAD11NM	WD18NM	LSOA11CD	epc_pc_A_C	retrofitSum
New Forest	Brockenhurst and Forest South East	E01022999	8.2	19,333,683.6
East Hampshire	Froxfield and Steep	E01022597	12.2	16,383,839.1
Isle of Wight	Cowes North	E01017300	40.3	15,404,574.4
Isle of Wight	Freshwater South	E01033239	26.1	14,972,795.5
East Hampshire	East Meon	E01022592	16.4	14,926,403.2
Isle of Wight	Nettlestone and Seaview	E01017351	21.0	14,534,696.0

5.2.5 Compare levy with costs

5.2.5.1 Scenario 1

Table 5.13 shows the overall results for Scenario 1.

Table 5.13: Summary of Scenario 1 (totals, £ m GBP)
LAD11NM	ons_hh_2020	Total_Retrofit_Cost_m	Total_dwellings	Total_dwellings_retrofited	Total_All_emissions_Levy_m	Total_Gas_emissions_Levy_m	Total_Elec_emissions_Levy_m	Total_Metered_Energy_emissions_Levy_m	Total_Energy_emissions_Levy_m	HouseholdsRetrofitted_pc	Total_All_emissions_Levy_pc	Total_Metered_emissions_Levy_pc	Total_Energy_emissions_Levy_pc	Years_to_pay_all_e
Basingstoke and Deane	73,924.0	563.1	77,059	39,871.9	481.1	39.9	21.2	61.1	61.1	51.7	85.4	10.8	10.8	1.2
East Hampshire	50,676.0	441.7	53,981	30,414.4	321.6	31.9	16.3	48.2	48.2	56.3	72.8	10.9	10.9	1.4
Eastleigh	56,431.0	408.6	56,751	29,750.8	310.1	32.1	13.9	46.0	46.0	52.4	75.9	11.3	11.3	1.3
Fareham	49,835.0	416.9	50,217	30,005.0	294.2	28.7	12.7	41.4	41.4	59.8	70.6	9.9	9.9	1.4
Gosport	37,398.0	297.0	37,565	21,188.2	167.9	16.9	9.1	26.0	26.0	56.4	56.6	8.8	8.8	1.8
Hart	38,300.0	315.6	40,188	22,514.6	292.6	28.8	11.7	40.5	40.5	56.0	92.7	12.8	12.8	1.1
Havant	55,050.0	461.1	55,902	32,886.5	245.5	30.8	13.9	44.8	44.8	58.8	53.2	9.7	9.7	1.9
Isle of Wight	65,591.0	685.2	72,393	45,809.7	231.8	32.3	18.3	50.6	50.6	63.3	33.8	7.4	7.4	3.0
New Forest	79,893.0	745.3	82,065	52,106.0	408.8	46.1	22.5	68.6	68.6	63.5	54.9	9.2	9.2	1.8
Portsmouth	90,188.0	802.0	90,855	57,205.5	370.8	42.4	20.8	63.1	63.1	63.0	46.2	7.9	7.9	2.2
Rushmoor	37,619.0	309.3	39,700	22,310.5	246.0	23.1	9.7	32.8	32.8	56.2	79.5	10.6	10.6	1.3
Southampton	101,661.0	908.1	108,605	62,731.8	435.3	44.8	26.7	71.4	71.4	57.8	47.9	7.9	7.9	2.1
Test Valley	52,432.0	441.2	55,679	30,534.4	317.9	24.9	16.4	41.2	41.2	54.8	72.1	9.3	9.3	1.4
Winchester	50,493.0	425.4	52,836	29,170.0	317.8	28.7	15.7	44.4	44.4	55.2	74.7	10.4	10.4	1.3

Table 5.13: Summary of Scenario 1 (per dwelling, £ m GBP)
LAD11NM	Retrofit_Cost_pdw	All_emissions_Levy_pdw	Gas_emissions_Levy_pdw	Elec_emissions_Levy_pdw	meteredEnergy_emissions_Levy_pdw	Energy_emissions_Levy_pdw	All_emissions_Levy_pdw_pc	meteredEnergy_emissions_Levy_pdw_pc	Energy_emissions_Levy_pdw_pc
Basingstoke and Deane	13,977.4	6,510.7	524.7	278.4	803.0	803.0	46.6	5.7	5.7
East Hampshire	14,334.8	6,249.2	605.0	298.8	903.8	903.8	43.6	6.3	6.3
Eastleigh	13,733.3	5,702.3	573.8	247.0	820.8	820.8	41.5	6.0	6.0
Fareham	13,884.2	6,054.9	572.9	253.6	826.5	826.5	43.6	6.0	6.0
Gosport	13,957.9	4,689.6	464.9	241.4	706.3	706.3	33.6	5.1	5.1
Hart	14,000.3	7,749.3	722.7	296.3	1,019.0	1,019.0	55.4	7.3	7.3
Havant	13,959.7	4,505.1	551.7	250.0	801.8	801.8	32.3	5.7	5.7
Isle of Wight	14,832.6	3,334.4	445.0	255.4	700.4	700.4	22.5	4.7	4.7
New Forest	14,192.6	5,238.0	563.9	276.8	840.7	840.7	36.9	5.9	5.9
Portsmouth	13,983.0	4,234.9	478.6	229.0	707.7	707.7	30.3	5.1	5.1
Rushmoor	13,851.1	6,600.3	595.3	245.8	841.1	841.1	47.7	6.1	6.1
Southampton	14,417.7	4,196.0	430.7	246.1	676.8	676.8	29.1	4.7	4.7
Test Valley	14,233.6	6,210.3	463.6	295.8	759.4	759.4	43.6	5.3	5.3
Winchester	14,388.0	6,239.7	549.6	294.4	844.0	844.0	43.4	5.9	5.9

## # Totals

## Total retrofit cost (£ m):

## [1] "7,221"

## Total dwellings retrofited:

## [1] "506,499"

## Out of (n elec meters):

## [1] "873,796"

## Out of (ONS n hh 2020):

## [1] "839,491"

## Total Year 1 levy (all):

## [1] "4,441"

## Total Year 1 levy (gas):

## [1] "451"

## Total Year 1 levy (elec):

## [1] "229"

If we exclude Southampton, Portsmouth & IoW the totals are…

## 
## Basingstoke and Deane        East Hampshire             Eastleigh 
##                     1                     1                     1 
##               Fareham               Gosport                  Hart 
##                     1                     1                     1 
##                Havant            New Forest              Rushmoor 
##                     1                     1                     1 
##           Test Valley            Winchester 
##                     1                     1

## # Totals

## Total retrofit cost (£ m):

## [1] "4,825"

## Total dwellings retrofited:

## [1] "340,752"

## Out of (n elec meters 2018):

## [1] "601,943"

## Out of (ons hh 2020):

## [1] "582,051"

## Total Year 1 levy (all):

## [1] "3,404"

## Total Year 1 levy (gas):

## [1] "332"

## Total Year 1 levy (elec):

## [1] "163"

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Repeat per dwelling

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

5.2.5.2 Scenario 2

Table 5.14 shows the overall results for Scenario 1.

Table 5.14: Summary of Scenario 2 (totals, £ m GBP)
LAD11NM	Total_Retrofit_Cost_m	Total_All_emissions_Levy_m_sc2	Total_Gas_emissions_Levy_m_sc2	Total_Elec_emissions_Levy_m_sc2	Total_All_emissions_Levy_pc
Basingstoke and Deane	563.1	399.3	27.2	15.1	70.9
East Hampshire	441.7	254.6	23.9	12.7	57.6
Eastleigh	408.6	232.9	21.8	8.5	57.0
Fareham	416.9	233.2	19.5	8.1	55.9
Gosport	297.0	111.5	10.1	5.5	37.5
Hart	315.6	267.6	23.9	8.8	84.8
Havant	461.1	160.1	20.7	8.7	34.7
Isle of Wight	685.2	125.6	18.7	11.7	18.3
New Forest	745.3	292.5	32.2	15.7	39.2
Portsmouth	802.0	233.4	24.9	11.9	29.1
Rushmoor	309.3	204.6	15.9	6.0	66.2
Southampton	908.1	274.4	25.9	16.3	30.2
Test Valley	441.2	254.3	16.3	12.4	57.6
Winchester	425.4	257.5	20.7	12.0	60.5

Table 5.14: Summary of Scenario 2 (per dwelling, £ m GBP)
LAD11NM	Retrofit_Cost_pdw	All_emissions_Levy_pdw	Gas_emissions_Levy_pdw	Elec_emissions_Levy_pdw	All_emissions_Levy_pdw_pc
Basingstoke and Deane	13,977.4	5,551.1	361.8	200.5	39.7
East Hampshire	14,334.8	5,136.3	455.6	230.2	35.8
Eastleigh	13,733.3	4,415.6	392.9	152.1	32.2
Fareham	13,884.2	4,914.7	390.3	161.6	35.4
Gosport	13,957.9	3,208.7	280.9	145.2	23.0
Hart	14,000.3	7,314.3	606.6	224.9	52.2
Havant	13,959.7	2,978.5	369.5	156.2	21.3
Isle of Wight	14,832.6	1,837.6	257.6	165.2	12.4
New Forest	14,192.6	3,872.6	394.1	196.3	27.3
Portsmouth	13,983.0	2,729.3	284.9	130.7	19.5
Rushmoor	13,851.1	5,704.4	418.8	151.8	41.2
Southampton	14,417.7	2,729.3	254.3	150.9	18.9
Test Valley	14,233.6	5,156.4	313.4	224.6	36.2
Winchester	14,388.0	5,198.8	398.9	223.3	36.1

## # Totals

## Total Year 1 levy (all):

## [1] "3,301"

## Total Year 1 levy (gas):

## [1] "302"

## Total Year 1 levy (elec):

## [1] "153"

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Repeat per dwelling

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

5.2.6 Years to pay…

5.2.6.1 Scenario 1

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.062   2.097   2.789   3.162   3.867  10.307

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 5.21: All levy payback years (Scenario 1)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   9.215  15.360  17.941  19.020  20.921  57.639

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 5.22: Payback if equal share (Scenario 1)

Figure 5.23: Payback if equal share (Scenario 1)

What happens in Year 2 totally depends on the rate of upgrades…

5.2.6.2 Scenario 2

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.924   2.502   4.376   5.341   7.663  20.699

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 5.24: All levy payback years (Scenario 2)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   9.215  15.360  17.941  19.020  20.921  57.639

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 5.25: Payback if equal share (Scenario 2)

Figure 5.26: Payback if equal share (Scenario 2)

What happens in Year 2 totally depends on the rate of upgrades…

5.2.7 Summary of results

Table 5.15: Summary of Scenario 1 (totals, £ m GBP)
LAD11NM	Total_dwellings	Total_dwellings_retrofited	Total_Retrofit_Cost_m	Total_All_emissions_Levy_m	Total_Metered_Energy_emissions_Levy_m	YearsToPayViaMeteredEnergyEmissions
Basingstoke and Deane	77,059	39,871.9	563.1	481.1	61.1	9.2
East Hampshire	53,981	30,414.4	441.7	321.6	48.2	9.2
Eastleigh	56,751	29,750.8	408.6	310.1	46.0	8.9
Fareham	50,217	30,005.0	416.9	294.2	41.4	10.1
Gosport	37,565	21,188.2	297.0	167.9	26.0	11.4
Hart	40,188	22,514.6	315.6	292.6	40.5	7.8
Havant	55,902	32,886.5	461.1	245.5	44.8	10.3
Isle of Wight	72,393	45,809.7	685.2	231.8	50.6	13.5
New Forest	82,065	52,106.0	745.3	408.8	68.6	10.9
Portsmouth	90,855	57,205.5	802.0	370.8	63.1	12.7
Rushmoor	39,700	22,310.5	309.3	246.0	32.8	9.4
Southampton	108,605	62,731.8	908.1	435.3	71.4	12.7
Test Valley	55,679	30,534.4	441.2	317.9	41.2	10.7
Winchester	52,836	29,170.0	425.4	317.8	44.4	9.6

Table 5.16: Summary of Scenario 1 (totals, £ m GBP)
Total_dwellings	Total_dwellings_retrofited	Total_Retrofit_Cost_m	Total_All_emissions_Levy_m	Total_Metered_Energy_emissions_Levy_m
873,796	506,499.4	7,220.7	4,441.5	680.2

Figure 5.27 shows the year 1 levy under each scenario against total estimated retrofit costs.

Figure 5.27: Compare districts

Figure 5.28

Figure 5.28: Pay back scatter for all LSOAs

6 R environment

6.1 R packages used

base R (R Core Team 2016)
bookdown (Xie 2016a)
data.table (Dowle et al. 2015)
ggplot2 (Wickham 2009)
kableExtra (Zhu 2018)
knitr (Xie 2016b)
rmarkdown (Allaire et al. 2018)
skimr (Arino de la Rubia et al. 2017)

6.2 Session info

7 Data Tables

I don’t know if this will work…

## Doesn't

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, and Winston Chang. 2018. Rmarkdown: Dynamic Documents for r. https://CRAN.R-project.org/package=rmarkdown.

Arino de la Rubia, Eduardo, Hao Zhu, Shannon Ellis, Elin Waring, and Michael Quinn. 2017. Skimr: Skimr. https://github.com/ropenscilabs/skimr.

Dowle, M, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, and E Antonyan. 2015. Data.table: Extension of Data.frame. https://CRAN.R-project.org/package=data.table.

R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.

Xie, Yihui. 2016a. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown.

———. 2016b. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://CRAN.R-project.org/package=knitr.

Zhu, Hao. 2018. kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.

Simulating a local emissions levy to fund local energy effiency retrofit

‘Wider Solent’ / ‘Pan-Hampshire’ as a case study

Ben Anderson (b.anderson@soton.ac.uk)

Last run at: 2021-11-01 11:57:13

1 About

2 Citing this data notebook

3 Highlights

4 Introduction

5 Emissions Levy Case Study - Wider Solent/‘Pan-Hampshire’

5.1 Data and boundary files

5.1.1 Useful LSOA labels and codes

5.1.2 Boundaries

5.1.3 IMD 2019

5.1.4 Fuel poverty

5.2 CREDS place-based emmissions estimates

5.2.1 Estimate per dwelling emissions

5.2.1.1 All emissions

5.2.1.2 Home energy use

5.2.1.3 Transport

5.2.2 Impute EPC counts

5.2.3 Estimating the annual emissions levy

5.2.3.1 Scenario 1: central cost

5.2.3.2 Scenario 2: Rising block tariff

5.2.4 Estimate retofit costs

5.2.5 Compare levy with costs

5.2.5.1 Scenario 1

5.2.5.2 Scenario 2

5.2.6 Years to pay…

5.2.6.1 Scenario 1

5.2.6.2 Scenario 2

5.2.7 Summary of results

6 R environment

6.1 R packages used

6.2 Session info

7 Data Tables

References